In this thesis I report the development of FARSA (Framework for Autonomous Robotics Simulation and Analysis), a simulation tool for the study of the interaction between language and action in cognitive robots and more in general for experiments in embodied cognitive science. Before presenting the tools, I will describe a series of experiments that involve simulated humanoid robots that acquire their behavioural and language skills autonomously through a trial-and-error adaptive process in which random variations of the free parameters of the robots’ controller are retained or discarded on the basis of their effect on the overall behaviour exhibited by the robot in interaction with the environment. More specifically the first series of experiments shows how the availability of linguistic stimuli provided by a caretaker, that indicate the elementary actions that need to be carried out in order to accomplish a certain complex action, facilitates the acquisition of the required behavioural capacity. The second series of experiments shows how a robot trained to comprehend a set of command phrases by executing the corresponding appropriate behaviour can generalize its knowledge by comprehending new, never experienced sentences, and by producing new appropriate actions. Together with their scientific relevance, these experiments provide a series of requirements that have been taken into account during the development of FARSA. The objective of this project is that to reduce the complexity barrier that currently discourages part of the researchers interested in the study of behaviour and cognition from initiating experimental activity in this area. FARSA is the only available tools that provide an integrated framework for carrying on experiments of this type, i.e. it is the only tool that provides ready to use integrated components that enable to define the characteristics of the robots and of the environment, the characteristics of the robots’ controller, and the characteristics of the adaptive process. Overall this enables users to quickly setup experiments, including complex experiments, and to quickly start collecting results.

O presente trabalho propõe um novo modelo de rede neural artificial voltado a aplicações robóticas, em especial a tarefas de natureza espaço-temporal e de horizonte infinito. Este modelo apresenta três características que o tornam único e que foram tomadas como guia para a sua concepção: auto-organização, representação temporal e aprendizado construtivo. O algoritmo de aprendizagem auto-organizada incorpora todos os mecanismos que são básicos para a auto-organização: competição global, cooperação local e auto-amplificação seletiva. A rede neural é suprida com propriedades dinâmicas através de uma memória de curto prazo. A memória de curto prazo é inserida na estrutura da rede por meio de integradores e diferenciadores, os quais são implementados na camada de entrada da rede. Nesta abordagem existe uma evidente separação de papéis: a rede é responsável pela não-linearidade e a memória é responsável pelo tempo. A construção automática da arquitetura da rede neural é realizada de acordo com uma unidade de habituação. A unidade de habituação regula o crescimento e a poda de neurônios. O procedimento de inclusão, adaptação e remoção de conexões sinápticas é realizado conforme o método de aprendizado hebbiano competitivo. Em muitos problemas práticos, como os existentes na área da robótica, a auto-organização, a representação temporal e o aprendizado construtivo são fatores imprescindíveis para o sucesso da tarefa. A grande dificuldade e, ao mesmo tempo, a principal contribuição deste trabalho consiste em integrar tais tecnologias em uma arquitetura de rede neural artificial de maneira eficiente. Estudos de caso foram elaborados para validar e, principalmente, determinar as potencialidades e as limitações do modelo neural proposto. Os cenários abrangeram tarefas simples de classificação de padrões e segmentação temporal. Os resultados preliminares obtidos demonstraram a eficiência do modelo neural proposto frente às arquiteturas conexionistas existentes e foram considerados bastante satisfatórios com relação aos parâmetros avaliados. No texto são apresentados, também, alguns aspectos teóricos das ciências cognitivas, os fundamentos de redes neurais artificiais, o detalhamento de uma ferramenta de simulação robótica, conclusões, limitações e possíveis trabalhos futuros. / The present work proposes a new artificial neural network model suitable for robotic applications, in special to spatiotemporal tasks and infinite horizon tasks. This model has three characteristics which make it unique and are taken as means to guide its conception: self-organization, temporal representation and constructive learning. The algorithm of self-organizing learning incorporates all the mechanisms that are basic to the self-organization: global competition, local cooperation and selective self-amplification. The neural network is supplied with dynamic properties through a short-term memory. The short-term memory is added in the network structure by means of integrators and differentiators, which are implemented in the input layer of the network. In this approach exists an evident separation of roles: the network is responsible for the non-linearity and the memory is responsible for the time. The automatic construction of the neural network architecture is carried out taking into account habituation units. The habituation unit regulates the growing and the pruning of neurons. The procedure of inclusion, adaptation and removal of synaptic connections is carried out in accordance with competitive hebbian learning technique. In many practical problems, as the ones in the robotic area, self-organization, temporal representation and constructive learning are essential factors to the success of the task. The great difficulty and, at the same time, the main contribution of this work consists in the integration of these technologies in a neural network architecture in an efficient way. Some case studies have been elaborated to validate and, mainly, to determine the potentialities and the limitations of the proposed neural model. The experiments comprised simple tasks of pattern classification and temporal segmentation. Preliminary results have shown the good efficiency of the neural model compared to existing connectionist architectures and they have been considered sufficiently satisfactory with regard to the evaluated parameters. This text also presents some theoretical aspects of the cognitive science area, the fundamentals of artificial neural networks, the details of a robotic simulation tool, the conclusions, limitations and possible future works.

O presente trabalho propõe um novo modelo de rede neural artificial voltado a aplicações robóticas, em especial a tarefas de natureza espaço-temporal e de horizonte infinito. Este modelo apresenta três características que o tornam único e que foram tomadas como guia para a sua concepção: auto-organização, representação temporal e aprendizado construtivo. O algoritmo de aprendizagem auto-organizada incorpora todos os mecanismos que são básicos para a auto-organização: competição global, cooperação local e auto-amplificação seletiva. A rede neural é suprida com propriedades dinâmicas através de uma memória de curto prazo. A memória de curto prazo é inserida na estrutura da rede por meio de integradores e diferenciadores, os quais são implementados na camada de entrada da rede. Nesta abordagem existe uma evidente separação de papéis: a rede é responsável pela não-linearidade e a memória é responsável pelo tempo. A construção automática da arquitetura da rede neural é realizada de acordo com uma unidade de habituação. A unidade de habituação regula o crescimento e a poda de neurônios. O procedimento de inclusão, adaptação e remoção de conexões sinápticas é realizado conforme o método de aprendizado hebbiano competitivo. Em muitos problemas práticos, como os existentes na área da robótica, a auto-organização, a representação temporal e o aprendizado construtivo são fatores imprescindíveis para o sucesso da tarefa. A grande dificuldade e, ao mesmo tempo, a principal contribuição deste trabalho consiste em integrar tais tecnologias em uma arquitetura de rede neural artificial de maneira eficiente. Estudos de caso foram elaborados para validar e, principalmente, determinar as potencialidades e as limitações do modelo neural proposto. Os cenários abrangeram tarefas simples de classificação de padrões e segmentação temporal. Os resultados preliminares obtidos demonstraram a eficiência do modelo neural proposto frente às arquiteturas conexionistas existentes e foram considerados bastante satisfatórios com relação aos parâmetros avaliados. No texto são apresentados, também, alguns aspectos teóricos das ciências cognitivas, os fundamentos de redes neurais artificiais, o detalhamento de uma ferramenta de simulação robótica, conclusões, limitações e possíveis trabalhos futuros. / The present work proposes a new artificial neural network model suitable for robotic applications, in special to spatiotemporal tasks and infinite horizon tasks. This model has three characteristics which make it unique and are taken as means to guide its conception: self-organization, temporal representation and constructive learning. The algorithm of self-organizing learning incorporates all the mechanisms that are basic to the self-organization: global competition, local cooperation and selective self-amplification. The neural network is supplied with dynamic properties through a short-term memory. The short-term memory is added in the network structure by means of integrators and differentiators, which are implemented in the input layer of the network. In this approach exists an evident separation of roles: the network is responsible for the non-linearity and the memory is responsible for the time. The automatic construction of the neural network architecture is carried out taking into account habituation units. The habituation unit regulates the growing and the pruning of neurons. The procedure of inclusion, adaptation and removal of synaptic connections is carried out in accordance with competitive hebbian learning technique. In many practical problems, as the ones in the robotic area, self-organization, temporal representation and constructive learning are essential factors to the success of the task. The great difficulty and, at the same time, the main contribution of this work consists in the integration of these technologies in a neural network architecture in an efficient way. Some case studies have been elaborated to validate and, mainly, to determine the potentialities and the limitations of the proposed neural model. The experiments comprised simple tasks of pattern classification and temporal segmentation. Preliminary results have shown the good efficiency of the neural model compared to existing connectionist architectures and they have been considered sufficiently satisfactory with regard to the evaluated parameters. This text also presents some theoretical aspects of the cognitive science area, the fundamentals of artificial neural networks, the details of a robotic simulation tool, the conclusions, limitations and possible future works.

O presente trabalho propõe um novo modelo de rede neural artificial voltado a aplicações robóticas, em especial a tarefas de natureza espaço-temporal e de horizonte infinito. Este modelo apresenta três características que o tornam único e que foram tomadas como guia para a sua concepção: auto-organização, representação temporal e aprendizado construtivo. O algoritmo de aprendizagem auto-organizada incorpora todos os mecanismos que são básicos para a auto-organização: competição global, cooperação local e auto-amplificação seletiva. A rede neural é suprida com propriedades dinâmicas através de uma memória de curto prazo. A memória de curto prazo é inserida na estrutura da rede por meio de integradores e diferenciadores, os quais são implementados na camada de entrada da rede. Nesta abordagem existe uma evidente separação de papéis: a rede é responsável pela não-linearidade e a memória é responsável pelo tempo. A construção automática da arquitetura da rede neural é realizada de acordo com uma unidade de habituação. A unidade de habituação regula o crescimento e a poda de neurônios. O procedimento de inclusão, adaptação e remoção de conexões sinápticas é realizado conforme o método de aprendizado hebbiano competitivo. Em muitos problemas práticos, como os existentes na área da robótica, a auto-organização, a representação temporal e o aprendizado construtivo são fatores imprescindíveis para o sucesso da tarefa. A grande dificuldade e, ao mesmo tempo, a principal contribuição deste trabalho consiste em integrar tais tecnologias em uma arquitetura de rede neural artificial de maneira eficiente. Estudos de caso foram elaborados para validar e, principalmente, determinar as potencialidades e as limitações do modelo neural proposto. Os cenários abrangeram tarefas simples de classificação de padrões e segmentação temporal. Os resultados preliminares obtidos demonstraram a eficiência do modelo neural proposto frente às arquiteturas conexionistas existentes e foram considerados bastante satisfatórios com relação aos parâmetros avaliados. No texto são apresentados, também, alguns aspectos teóricos das ciências cognitivas, os fundamentos de redes neurais artificiais, o detalhamento de uma ferramenta de simulação robótica, conclusões, limitações e possíveis trabalhos futuros. / The present work proposes a new artificial neural network model suitable for robotic applications, in special to spatiotemporal tasks and infinite horizon tasks. This model has three characteristics which make it unique and are taken as means to guide its conception: self-organization, temporal representation and constructive learning. The algorithm of self-organizing learning incorporates all the mechanisms that are basic to the self-organization: global competition, local cooperation and selective self-amplification. The neural network is supplied with dynamic properties through a short-term memory. The short-term memory is added in the network structure by means of integrators and differentiators, which are implemented in the input layer of the network. In this approach exists an evident separation of roles: the network is responsible for the non-linearity and the memory is responsible for the time. The automatic construction of the neural network architecture is carried out taking into account habituation units. The habituation unit regulates the growing and the pruning of neurons. The procedure of inclusion, adaptation and removal of synaptic connections is carried out in accordance with competitive hebbian learning technique. In many practical problems, as the ones in the robotic area, self-organization, temporal representation and constructive learning are essential factors to the success of the task. The great difficulty and, at the same time, the main contribution of this work consists in the integration of these technologies in a neural network architecture in an efficient way. Some case studies have been elaborated to validate and, mainly, to determine the potentialities and the limitations of the proposed neural model. The experiments comprised simple tasks of pattern classification and temporal segmentation. Preliminary results have shown the good efficiency of the neural model compared to existing connectionist architectures and they have been considered sufficiently satisfactory with regard to the evaluated parameters. This text also presents some theoretical aspects of the cognitive science area, the fundamentals of artificial neural networks, the details of a robotic simulation tool, the conclusions, limitations and possible future works.

This dissertation clarifies the role and relevance of the body in social interaction and cognition from an embodied cognitive science perspective. Theories of embodied cognition have during the past two decades offered a radical shift in explanations of the human mind, from traditional computationalism which considers cognition in terms of internal symbolic representations and computational processes, to emphasizing the way cognition is shaped by the body and its sensorimotor interaction with the surrounding social and material world. This thesis develops a framework for the embodied nature of social interaction and cognition, which is based on an interdisciplinary approach that ranges historically in time and across different disciplines. The theoretical framework presents a thorough and integrated understanding that supports and explains the embodied nature of social interaction and cognition. It is argued that embodiment is the part and parcel of social interaction and cognition in the most general and specific ways, in which dynamically embodied actions themselves have meaning and agency. The framework is illustrated by empirical work that provides some detailed observational fieldwork on embodied actions captured in three different episodes of spontaneous social interaction in situ. Besides illustrating the theoretical issues discussed in the thesis, the empirical work also reveals some novel characteristics of embodied action in social interaction and cognition. Furthermore, the ontogeny of social interaction and cognition is considered, in which social scaffolding and embodied experience play crucial roles during child development. In addition, the issue what it would take for an artificial system to be (socially) embodied is discussed from the perspectives of cognitive modeling and technology. Finally, the theoretical contributions and implications of the study of embodied actions in social interaction and cognition for cognitive science and related disciplines are summed up. The practical relevance for applications to artificial intelligence and human-computer interaction is also outlined as well as some aspects for future work.

Submitted by Glauber Assunção Moreira (glauber.a.moreira@gmail.com) on 2018-08-24T18:33:40Z No. of bitstreams: 1 Dissertação final.pdf: 8319574 bytes, checksum: bfc7083d5976839ec23a4b05404171d7 (MD5) / Approved for entry into archive by Marly Santos (marly@ufba.br) on 2018-08-24T19:33:31Z (GMT) No. of bitstreams: 1 Dissertação final.pdf: 8319574 bytes, checksum: bfc7083d5976839ec23a4b05404171d7 (MD5) / Made available in DSpace on 2018-08-24T19:33:31Z (GMT). No. of bitstreams: 1 Dissertação final.pdf: 8319574 bytes, checksum: bfc7083d5976839ec23a4b05404171d7 (MD5) / O presente trabalho tem como objetivo analisar o espetáculo Embodied, de Cristian Duarte, usando como fundamentação teórica a Embodied Cognitive Science, sobretudo no que se refere ao estudo das metáforas, abordado por George Lakoff e Mark Johnson nas publicações de 1999(Philosophy in the Flesh-The Embodied Mind its Challenge to Western Thought) e 2002 (Metáforas da Vida Cotidiana). Relacionar dança e ciência foi a estratégia utilizada para a análise coreográfica, sobretudo porque o livro Philosophy in the Flesh foi um dos pontos de partida para a construção do espetáculo. Embodied sugere relações com o pensamento científico atual. Os padrões organizativos e estruturais da cena mostram-se conectados com as hipóteses filosóficas apontadas pelos autores Lakoff e Johnson, que propõem a mudança paradigmática sobre a natureza da razão, afirmam o conceito de pensamento metafórico, a incorporação da mente (embodied mind) e sugerem, ainda, que o pensamento, assim como as ações, decorrem do sistema sensório-motor, porém se manifestam de maneiras diferentes. A cognição é o espaço onde o corpo, o ambiente e o cérebro estão acoplados densamente. Desta maneira, a metáfora torna-se uma importante ferramenta cognitiva. Nesta pesquisa, são identificadas três metáforas principais usadas pelos dançarinos durante o espetáculo: o corpo-coisa, o corpo-embate e o corpo-pornô. Observa-se que tais metáforas se estabelecem através de manipulações entre um corpo e outro, do campo de forças criado entre os corpos dos dançarinos, caracterizado por relações de polarização, relações diádicas e vetores de ação de movimento que não tem necessariamente continuidade de um corpo para o outro. As metáforas dançadas apontadas na pesquisa atuam o tempo todo durante o espetáculo e se estruturam nos conceitos de experiências e julgamentos subjetivos dos intérpretes. Observa-se que os dançarinos estão sugerindo metáforas como estratégias de pensamento e ação e que eles atuam como agentes metafóricos que compreendem e experimentam uma coisa em relação à outra. A análise buscou examinar possibilidades de relações entre os conhecimentos artísticos e científicos por acreditar serem sutis as interfaces entre essas duas áreas de conhecimento. / This research work aims to analyse the Embodied performance from Cristian Duarte, by using the theoretical foundation of the Embodied Cognitive Science for the study of the metaphors, which have previously been proposed by George Lakoff and Mark Johnson in their publications in 1999 (Philosophy in the Flesh-The Embodied Mind its Challenge to Western Thought) and 2002 (Methaphors we live by). The initiative to carry out a choreographic analysis based on the relationship between dance and science was undertaken in view of the fact that the book Philosophy in the Flesh was one of the starting points for the creation of the performance. Essentially, Embodied suggests relationships between the performance and the current scientific thinking on cognition. Moreover, the organisational and structural patterns of the scenes can be considered connected to the philosophical hypothesis pointed out by the authors, Lakoff and Johnson, who have proposed a paradigm change in the nature of reasoning by surrounding the metaphor concept of thinking with the incorporation of the mind (embodied mind). They suggest that thoughts, as well as actions are produced by the sensory motor system, but manifested in different ways. Cognition is the space where the body, its surroundings, and the mind are tightly joined together. Therefore, the metaphor becomes an important cognitive instrument. In this research work, three main metaphors are used to represent the ballet dancers during the Embodied performance. They are body-thing, body-collision, and body-porno. The research has revealed that the collisions between bodies, and the force field created by them, establish the metaphors that are characterised by polarised relations of actions, which do not necessarily demonstrate continuity from one body to another. In addition, the three metaphors identified in this research, are put into action at every point in time of the performance. They are structured on the basis of the experiences and subjective judgements of the interpreters. Finally, this research demonstrates that the ballet dancers are applying these metaphors as strategies of thoughts and actions. They actually perform as metaphor agents who understand and experiment one thing in relation to another. In conclusion, this analysis has shown the possible relationship between artistic and scientific knowledge, since the interfaces between these two realms of knowledge are analogous.

Behavioural scientists argue that ‘social learning' provides the link between biological phenomena and cultural phenomena because of its role in the ‘cultural transmission' of knowledge among individuals within and across generations. However, leading authors within the social sciences have proposed alternative ways of thinking about social life not founded on the Modern oppositions including nature-culture, biology-culture, body-mind, and individual-society. Similarly, the distinction between a domain of nature and a domain of nurture has also been extensively criticized within biology. Finally, advocates of ‘radical embodied cognitive science' offer an alternative to the representational-computational view of the mind which supports the conventional notion of culture and cultural information. This thesis attempts to integrate developmental systems theory, radical embodied cognitive science, and relational thinking, with the goal to bring the field of social learning closer to these critical theoretical developments. In Chapter 2, I find no justification for the claim that the genome carries information in the sense of specification of biological form. Chapter 3 presents a view of ontogeny as a historical, relational, constructive and contingent process. Chapter 4 uses the notions of environmental information, abilities, affordances, and intentions to make sense of behaviour and learning. In Chapter 5, I argue that the notion of social learning can be understood in terms of relational histories of development rather than in terms of transmission of information. I then report empirical studies investigating behavioural coordination and social learning consistent with this theoretical framework. Chapter 6 presents evidence that dyads in a joint making activity synchronize their attention constrained by their changing situation and that coordination of attention is predictive of implicit and explicit learning. Chapter 7 presents evidence that joint attention does not require gaze following and that attentional coordination is predictive of learning a manual task. Together, these theoretical and empirical studies suggest a new way of thinking about how humans and other animals live and learn socially, one that is consistent with critical theoretical and philosophical developments that are currently neglected in the literature on social learning.